Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We evaluate OCTOPUS AIM VCT 2 PLC prediction models with Ensemble Learning (ML) and Linear Regression1,2,3,4 and conclude that the LON:OSEC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:OSEC stock.

Keywords: LON:OSEC, OCTOPUS AIM VCT 2 PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What are buy sell or hold recommendations?
2. What is the use of Markov decision process?
3. Why do we need predictive models?

## LON:OSEC Target Price Prediction Modeling Methodology

One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. We consider OCTOPUS AIM VCT 2 PLC Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:OSEC stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

F(Linear Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+1 year) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of LON:OSEC stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

How do AC Investment Research machine learning (predictive) algorithms actually work?

## LON:OSEC Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:OSEC OCTOPUS AIM VCT 2 PLC
Time series to forecast n: 25 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:OSEC stock.

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Yellow to Green): *Technical Analysis%

## Conclusions

OCTOPUS AIM VCT 2 PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Linear Regression1,2,3,4 and conclude that the LON:OSEC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:OSEC stock.

### Financial State Forecast for LON:OSEC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 7535
Market Risk3044
Technical Analysis6586
Fundamental Analysis6853
Risk Unsystematic4862

### Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 837 signals.

## References

1. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
2. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
3. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
4. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
5. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
6. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
7. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:OSEC stock?
A: LON:OSEC stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Linear Regression
Q: Is LON:OSEC stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:OSEC Stock.
Q: Is OCTOPUS AIM VCT 2 PLC stock a good investment?
A: The consensus rating for OCTOPUS AIM VCT 2 PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:OSEC stock?
A: The consensus rating for LON:OSEC is Hold.
Q: What is the prediction period for LON:OSEC stock?
A: The prediction period for LON:OSEC is (n+1 year)